Plot Gallery

Workspace and Other Setup

@TODO

• Error bars
• More facets
• More labelling functions
• Interactive (i.e. htmlwidgets)
• Maps
• Conditional labelling

Packages & settings

Load what I need.

library(wso)
library(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
wso::util.settings()

Modify data

To make our data more convenient to work with

miris <- iris %>%
  mutate_if(
    is.numeric, ~ . * 100
  ) %>%
  mutate(
    wsoIndex = 1:nrow(iris)
  )

fuelEconomy <- mpg %>%
  group_by(year, cyl) %>%
  summarize(
    avgHwy = mean(hwy)
  )

monthlyStorms <- storms %>% 
  mutate(
    year = make_date(year)
  ) %>%
  group_by(year, name) %>% 
  summarise(
    meanPressure = mean(pressure),
    meanWind = mean(wind)
  ) %>%
  group_by(year) %>% 
  summarise(
    count = dplyr::n(),
    meanPressure = mean(meanPressure),
    meanWind = mean(meanWind)
  ) %>% 
  mutate(
    studyPeriod = if_else(
      year >= make_date(2000) & year <= make_date(2010),
      TRUE, FALSE
    )
  ) %>%
  filter(
    year >= make_date(1998) & year <= make_date(2012)
  )

huron <- tibble(
  year = make_date(1875:1972),
  level = as.vector(LakeHuron)
)

Themes and colors

Make my own theme!

theme_cs <- theme_minimal() +
  theme(
    
    ## Title
    plot.title = element_text(
      size = 14, face = "bold", hjust = 0.5,
      margin = margin(t = 10, r = 0, b = 10, l = 0)
    ),
    
    ## Axis
    axis.text = element_text(size = 10),
    axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 10, l = 0)),
    axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 10)),
    
    ## Legend
    legend.title = element_text(face = "bold"),
    legend.text = element_text(size = 10)
    
  )

Bar

Category on x axis

mpg %>%
  group_by(class) %>%
  summarise(
    avgHwy = mean(hwy)
  ) %>%
  ggplot() +
    geom_col(
      aes(x = class, y = avgHwy), fill = wso::color.wsoCols("dark blue")
    ) +
    labs(
      title = "Fuel Economy by Class",
      x = "Class",
      y = "MPG"
    ) +
    theme_cs

Reordering and labeller

mpg %>%
  group_by(class) %>%
  summarise(
    avgHwy = mean(hwy)
  ) %>%
  ggplot() +
    geom_col(
      aes(
        x = forcats::fct_reorder(class, avgHwy),
        y = avgHwy
      ), 
      fill = wso::color.wsoCols("dark blue")
    ) +
    labs(
      title = "Fuel Economy by Class",
      x = "Class",
      y = "MPG"
    ) +
    theme_cs + 
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1)
    )

Switch the order, add some labels, and make sure our y axis tops out over our maximum value.

cars <- mpg %>%
  group_by(class) %>%
  summarise(
    avgHwy = mean(hwy)
  ) 

cars %>%
  ggplot() +
    geom_col(
      aes(
        x = forcats::fct_reorder(class, avgHwy, .desc = TRUE),
        y = avgHwy
      ), fill = wso::color.wsoCols("dark blue")
    ) +
    scale_y_continuous( 
      limits = c(0, plyr::round_any(max(cars$avgHwy), 10, ceiling))
    ) +
    geom_text(
      aes(
        x = forcats::fct_reorder(class, avgHwy, .desc = TRUE), 
        y = avgHwy - 2,
        label = scales::comma_format(.01)(avgHwy)
      ),
      size = 3, colour = "#FEFFFE", fontface = "bold"
    ) +
    labs(
      title = "Fuel Economy by Class",
      x = "Class",
      y = "MPG"
    ) +
    theme_cs + 
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1)
    )

Date on x axis

monthlyStorms %>%
  ggplot() +
    geom_col(
      aes(x = year, y = count), fill = wso::color.wsoCols("dark blue")
    ) +
    scale_x_date(
      labels = scales::date_format("%Y"),
      date_breaks = "3 years"
    ) +
    labs(
      title = "Storms in US Per Year",
      x = "Year",
      y = "Count of storms"
    ) +
    theme_cs

Fill by Group

monthlyStorms %>%
  ggplot() +
    geom_col(
      aes(x = year, y = count, fill = studyPeriod)
    ) +
    scale_fill_manual(
      name = "Event",
      breaks = c("TRUE", "FALSE"),
      labels = c("In Study Period", "Out of Study Period"),
      values = c(
        "TRUE" = unname(wso::color.wsoCols("dark blue")),
        "FALSE" = unname(wso::color.wsoCols("light grey"))
      )
    ) +
    labs(
      title = "Storms in US Per Year",
      x = "Year",
      y = "Count of storms"
    ) +
    theme_cs

Stacked

storms %>%
  ggplot() +
    geom_bar(
      aes(x = make_date(year), fill = status)
    ) +
    wso::color.scale_fill_wso(
      name = "",
      labels = stringr::str_to_title
    ) +
    labs(
      title = "Storms in US Per Year",
      x = "Year",
      y = "Count of storms"
    ) +
    theme_cs +
    theme(
      legend.position = "bottom"
    )

Stacked (Proportions)

storms %>%
  ggplot() +
    geom_bar(
      aes(x = make_date(year), fill = status),
      position = "fill"
    ) +
    scale_y_continuous(
      labels = scales::percent_format(1)
    ) +
    wso::color.scale_fill_wso(
      name = "",
      labels = stringr::str_to_title
    ) +
    labs(
      title = "Type of storms in US Per Year",
      x = "Year",
      y = "Percent"
    ) +
    theme_cs +
    theme(
      legend.position = "bottom"
    )

Side by side

fuelEconomy %>%
  ggplot(
    ## Include in here so it is given to all layers
    ## Make cyl a factor so we can use with bar chart
    aes(x = as.factor(year), y = avgHwy, fill = as.factor(cyl)) 
  ) +
    geom_bar(
      # Put the bars of different groups next to each other instead of stacked
      position = "dodge", stat = "identity"
    ) +
    # Gives us a little breathing room up to so our labels aren't cut off
    geom_blank( 
      aes(y = avgHwy * 1.1)
    ) +
    labs(
      title = "Fuel Economy Over the Years",
      y = "Fuel Economy (mpg)",
      x = "Year"
    ) +
    wso::color.scale_fill_wso(
      name = "Drive Type"
    ) +
    theme_cs

Now adjust the column names and add labels to make sure our legend colors and plots fill colors match.

fuelEconomy %>%
  ggplot(
    aes(x = as.factor(year), y = avgHwy, fill = as.factor(cyl))
  ) +
    geom_bar(
      position = "dodge", stat = "identity"
    ) +
    geom_text(
      # Specify data for annotations
      aes(label = factor(cyl)), 
      # Space the labels so they aren't stacked
      position = position_dodge(width = 0.9), 
      # Bump the annotation up
      vjust = -1
    ) +
    # Gives us a little breathing room up to so our labels aren't cut off
    geom_blank( 
      aes(y = avgHwy * 1.1)
    ) +
    labs(
      title = "Fuel Economy Over the Years",
      y = "Fuel Economy (mpg)",
      x = "Year"
    ) +
    wso::color.scale_fill_wso(
      name = "Drive Type",
      palette = "dark blue to green",
      interpolate = TRUE,
      labels = c("Four", "Five", "Six", "Eight")
      #labels = c("Eight", "Six", "Five", "Four") ## Watch out this is out of order and you wouldn't know it
    ) +
    theme_cs

Sideways

mysleep <- msleep %>%
  arrange(desc(sleep_total)) %>%
  slice(50:70) %>%
  mutate(
    wellRested = if_else(
      sleep_total >= 8, "> 8 Hours", "< 8 Hours"
    )
  )

mysleep %>%
  ggplot() +
    geom_col(
      aes(
        x = forcats::fct_reorder(name, sleep_total), 
        y = sleep_total, 
        fill = wellRested
      )
    ) +
    coord_flip() +
    labs(
      title = "How well rested are these animals?",
      y = "Hours of sleep",
      x = "Animal"
    ) +
    scale_y_continuous(
      breaks = seq(0, max(mysleep$sleep_total), 2)
    ) +
    scale_fill_manual(
      name = "Hours of \nsleep per day",
      breaks = c( "> 8 Hours", "< 8 Hours"),
      values = c(
        "> 8 Hours" = unname(wso::color.wsoCols("dark blue")), 
        "< 8 Hours" = unname(wso::color.wsoCols("burnt orange"))
      )
    ) +
    theme_cs

Point / Scatter

Basic

miris %>%
  ggplot() +
    geom_point(
      aes(x = Sepal.Width, y = Petal.Width, color = Species),
      size = 3
    ) +
    scale_x_continuous(
      labels = scales::comma_format()
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Sepal Width by Petal Length",
      x = "Sepal.Width (small units)",
      y = "Petal.Length (small units)"
    ) +
    wso::color.scale_color_wso() +
    theme_cs

Annotate specific data points

selectGroup <- miris %>%
  filter(
    Species == "virginica",
    Sepal.Width > 3750  
  )

selectPoint <- miris %>%
  filter(
    Petal.Width + Sepal.Width == min(Petal.Width + Sepal.Width)
  )

miris %>%
  ggplot(
    aes(x = Petal.Length, y = Sepal.Width)
  ) +
    geom_point(
      aes(color = Species), size = 3
    ) +
    geom_point(data = selectGroup, size = 3, shape = 1) +
    geom_point(data = selectPoint, size = 3, shape = 1) +
    geom_text_repel(data = selectGroup, mapping = aes(label = wsoIndex), point.padding = 1) +
    geom_label_repel(data = selectPoint, mapping = aes(label = wsoIndex), point.padding = 1) +
    scale_x_continuous(
      labels = scales::comma_format()
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Sepal Width by Petal Length",
      x = "Petal.Length (small units)",
      y = "Sepal.Width (small units)"
    ) +
    wso::color.scale_color_wso() +
    theme_cs

Legend label adjustments

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status))
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms",
      y = "Pressure (mbar)",
      x = "Wind Speed (mph)"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs

Jitter

mpg %>%
  ggplot() + 
  geom_jitter(
    aes(x = cty, y = hwy, color = class),
    width = 0.5, height = 0.5
  ) +
  wso::color.scale_color_wso(
    name = "Class",
    labels = stringr::str_to_title
  ) +
  labs(
    title = "Fuel Economy",
    y = "Highway MPG",
    x = "City MPG"
  ) +
  theme_cs

Lump together classes that appear less than 15% of the time

mpg %>%
  ggplot() + 
  geom_jitter(
    aes(
      x = cty, 
      y = hwy, 
      color = forcats::fct_lump(class, prop = 0.15)
    ),
    width = 2, height = 2
  ) +
  wso::color.scale_color_wso(
    name = "Class",
    labels = stringr::str_to_title
  ) +
  labs(
    title = "Fuel Economy",
    y = "Highway MPG",
    x = "City MPG"
  ) +
  theme_cs

Count

mpg %>%
  ggplot() + 
  geom_count(
    aes(x = cty, y = hwy), color = wso::color.wsoCols("dark blue")
  ) +
  labs(
    title = "Fuel Economy",
    y = "Highway MPG",
    x = "City MPG"
  ) +
  theme_cs

Rug

mtcars %>%
  ggplot(
      aes(x = wt, y = mpg)
    ) +
    geom_point(
      color = wso::color.wsoCols("blue")
    ) +
    geom_rug() +
    coord_cartesian(
      ylim = c(0,  plyr::round_any(max(mtcars$mpg), 10, f = ceiling))
    ) +
    labs(
      title = "Fuel Economy by Weight",
      y = "Highway MPG",
      x = "Weight (tons)"
    ) +
    theme_cs

Regression

Manual

model <- lm(weight ~ Time, data = ChickWeight)
int <- coef(model)["(Intercept)"] %>% unname()
slope <- coef(model)["Time"] %>% unname()

ChickWeight %>%
  ggplot() +
    geom_abline(
      intercept = int, slope = slope, color = wso::color.wsoCols("dark blue"),
    ) +
    geom_point(
      aes(x = Time, y = weight, color = Diet)
    ) +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Geom Smooth

ChickWeight %>%
  ggplot(
      aes(x = Time, y = weight, color = Diet)
    ) +
    geom_smooth(
      se = FALSE, method = lm
    ) +
    geom_point() +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Box plot

ChickWeight %>%
  ggplot() +
    geom_boxplot(
      aes(x = Diet, y = weight),
      outlier.colour = "red", outlier.shape = 1
    ) +
    labs(
      title = "Chick Weight By Diet",
      x = "Diet",
      y = "Weight (grams)"
    ) +
    theme_cs

With the original data

ChickWeight %>%
  ggplot(
      aes(x = Diet, y = weight)
    ) +
    geom_jitter(
      aes(color = Diet)
    ) +
    geom_boxplot(
      outlier.shape = NA, alpha = 0
    ) +
    labs(
      title = "Chick Weight By Diet",
      x = "Diet",
      y = "Weight (grams)"
    ) +
    theme_cs

Histogram

diamonds %>%
  ggplot() +
    geom_histogram(
      aes(x = price), fill = wso::color.wsoCols("dark blue"),
      bins = 35
      # binwidth = 0.05 ## Could use this too
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    scale_x_continuous(
      labels = scales::dollar_format()
    ) +
    labs(
      title = "Diamonds",
      x = "Price",
      y = "Count"
    ) +
    theme_cs

diamonds %>%
  ggplot() +
    geom_histogram(
      aes(x = price, fill = cut),
      bins = 35
      # binwidth = 0.05 ## Could use this too
    ) +
    wso::color.scale_fill_wso(
      name = "Price"
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    scale_x_continuous(
      labels = scales::dollar_format()
    ) +
    labs(
      title = "Diamonds",
      x = "Price",
      y = "Count"
    ) +
    theme_cs

Frequency

diamonds %>%
  ggplot() +
    geom_freqpoly(
      aes(x = price, color = cut),
      bins = 35
      # binwidth = 0.05 ## Could use this too
    ) +
    wso::color.scale_color_wso(
      name = "Price"
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    scale_x_continuous(
      labels = scales::dollar_format()
    ) +
    labs(
      title = "Diamonds",
      x = "Price",
      y = "Count"
    ) +
    theme_cs

diamonds %>%
  ggplot() +
    geom_freqpoly(
      aes(x = price, color = cut, stat(density)),
      bins = 35
      # binwidth = 0.05 ## Could use this too
    ) +
    wso::color.scale_color_wso(
      name = "Price"
    ) +
    scale_y_continuous(
      labels = scales::percent_format(.01)
    ) +
    scale_x_continuous(
      labels = scales::dollar_format()
    ) +
    labs(
      title = "Diamonds",
      x = "Price",
      y = "Percent"
    ) +
    theme_cs

Density

diamonds %>%
  ggplot() +
    geom_density(
      aes(x = depth, color = cut, fill = cut),
      alpha = 0.1
    ) +
    coord_cartesian(
      xlim = c(55, 70)
    ) +
    wso::color.scale_color_wso(
      name = "Cut"
    ) +
    wso::color.scale_fill_wso(
      name = "Cut"
    ) +
    scale_y_continuous(
      labels = scales::percent_format(.01)
    ) +
    labs(
      title = "Diamonds",
      x = "Depth",
      y = "Percent"
    ) +
    theme_cs +
    theme(
      legend.position = "bottom"
    )

Facets

With historgrams and a label list

classList <- c(
  "compact" = "Compact",
  "midsize" = "Midsize",
  "suv" = "Sports Utility Vehicle (SUV)"
)

mpg %>%
  filter(
    class %in% c("compact", "midsize", "suv")
  ) %>%
  ggplot() + 
    geom_histogram(
      aes(x = cty),
      bins = 35,
      fill = wso::color.wsoCols("dark blue")
    ) +
    facet_grid(
      rows = vars(year), cols = vars(class),
      labeller = labeller(
        .cols = classList
      )
    ) +
    labs(
      title = "Fuel Economy",
      y = "Count of vehicles",
      x = "City MPG"
    ) +
    theme_cs

With a scatterplot and spaced facets

miris %>%
  ggplot() +
    geom_point(
      aes(x = Sepal.Width, y = Petal.Width, color = Species),
      size = 2
    ) +
    facet_grid(
      cols = vars(Species)
    ) +
    scale_x_continuous(
      labels = scales::comma_format()
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Sepal Width by Petal Length",
      x = "Sepal.Width (small units)",
      y = "Petal.Length (small units)"
    ) +
    wso::color.scale_color_wso() +
    theme_cs +
    theme(
      panel.spacing = unit(2, "lines"),
      strip.background = element_blank(),
      strip.text.x = element_blank(),
      legend.position = "bottom",
      axis.text.x = element_text(angle = 45, hjust = 1)
    )

Error Bars

Path

economics <- economics %>%
  mutate(
    unemployRate = unemploy/pop
  )

economics %>%
  ggplot() +
    geom_path(
      mapping = aes(unemployRate, y = psavert, color = as.numeric(date)),
      lineend = "butt", linejoin = "round", linemitre = 1
    ) +
    wso::color.scale_color_wso(
      discrete = FALSE,
      palette = "dark blue to green",
      name = "Year",
      labels = function(label) {
        labelDate <- as.Date(label, origin = "1970-01-01")
        year(labelDate)
      }
    ) +
    labs(
      title = "Unemployment vs Personal Savings Over Time",
      x = "Unemployment Rate",
      y = "Personal Savings"
    ) +
    theme_cs

Polygon

ids <- factor(c("A", "B", "C", "D", "E", "F"))

values <- tibble(
  id = ids,
  value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)

positions <- tibble(
  id = rep(ids, each = 4),
  x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
  0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
  y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
  2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)

datapoly <- left_join(values, positions, by = "id")

datapoly %>%
  ggplot() +
    geom_polygon(
      aes( x = x, y = y, fill = value, group = id)
    ) + 
    wso::color.scale_fill_wso(
      discrete = FALSE,
      palette = "blue green red",
      name = "Score"
    ) +
    labs(
      title = "No Clue What This Is",
      x = "A crazy variable",
      y = "Something wild"
    ) +
    theme_cs

Rectangle (Background Color)

Horizontal (Data outside ggplot)

Note that here we are defining our data outside of our main geom so that we can properly apply an alpha. Details here :https://stackoverflow.com/questions/17521438/geom-rect-and-alpha-does-this-work-with-hard-coded-values

Also note that with dates in the x-axis, we need to keep xmin and xmax out of aes() because it would try to coerce Inf as a date, which will not work. Alternatively, we could have specified min(monthlyStorms$date) and max(monthlyStorms$date) in our dataframe and kept xmin and xmax in our aes(), but then the colored background doesn’t extend to the edge of the plot, it only goes to the middle of the first and last bar, which isn’t the greatest looking thing in the world.

ggplot() +
  geom_rect(
    aes(ymin = -Inf, ymax = 8000), 
    xmin = -Inf, xmax = Inf, 
    alpha = 0.25, fill = wso::color.wsoCols("green")
  ) +
  geom_line(
    data = economics,
    aes(x = date, y = unemploy),
    color = wso::color.wsoCols("dark blue"), size = 0.75
  ) +
  scale_y_continuous(
    label = scales::comma_format(1)
  ) +
  labs(
    title = "US Unemployment",
    x = "Date",
    y = "People Out of Work"
  ) +
  theme_cs

Horizontal (Data insde ggplot)

To avoid having to specify your data in every layer, one work around is to provide a single row from the data set in your rectangle. This will prevent the rectangle from appearing once for each time in your dataset (which prevents you from being able to use alpha).

economics %>%
  ggplot() +
    geom_rect(
      data = economics[1,],
      aes(xmin = min(economics$date), xmax = max(economics$date), ymin = 8000, ymax = Inf), 
      alpha = 0.25, fill = wso::color.wsoCols("red")
    ) +
    geom_line(
      aes(x = date, y = unemploy),
      color = wso::color.wsoCols("dark blue"), size = 0.75
    ) +
    scale_y_continuous(
      label = scales::comma_format(1)
    ) +
    labs(
      title = "US Unemployment",
      x = "Date",
      y = "People Out of Work"
    ) +
    theme_cs

Horizontal (with data frame)

## No x min or max here because we want to use Inf, and can't make that jive with our date axis
## We'll specify them outside of aes in our geom_rect
backgrounds <- tibble(
  minY = c(0, 15),
  maxY = c(15, Inf),
  fillColor = c("Average", "Abnormal")
)

monthlyStorms %>%
  ggplot() +
    geom_rect(
      data = backgrounds, 
      aes(ymin = minY, ymax = maxY, fill = fillColor), 
      xmin = -Inf, xmax = Inf,
      alpha = .2
    ) +
    geom_col(
      aes(x = year, y = count), fill = unname(wso::color.wsoCols("dark blue"))
    ) +
    scale_fill_manual(
      name = "Frequency",
      aesthetics = c("fill"),
      values = c(
        "Average" = unname(wso::color.wsoCols("green")),
        "Abnormal" = unname(wso::color.wsoCols("red"))
      )
    ) +
    labs(
      title = "Storms in US Per Year",
      x = "Year",
      y = "Count of storms"
    ) +
    theme_cs

Vertical (by value)

Here we use dplyr::lead(date) to dynamically set our xmax… basically this grabs the next value in line.

economicsAdj <- economics %>%
  mutate(
    status = if_else(
      unemploy > 8000,
      "Recession", "Boon"
    )
  ) 

economicsAdj %>%
  ggplot() +
    geom_rect(
      aes(
        xmin = date, xmax = dplyr::lead(date),
        ymin = -Inf, ymax = Inf,
        fill = status
      ),
      alpha = 0.4
    ) +
    geom_line(
      aes(x = date, y = unemploy),
      color = wso::color.wsoCols("dark blue"), size = 0.75
    ) +
    scale_y_continuous(
      label = scales::comma_format(1)
    ) +
    labs(
      title = "US Unemployment",
      x = "Date",
      y = "People Out of Work"
    ) +
    theme_cs

Ribbon

huron %>%
  ggplot() +
    geom_ribbon(
      aes(x = year, ymin = level - 1, ymax = level + 1), 
      fill = wso::color.wsoCols("blue"), alpha = 0.4
    ) + 
    geom_line(
      aes(x = year, y = level), 
      color = wso::color.wsoCols("dark blue"), size = 1
    ) +
    labs(
      title = "Level of Lake Huron",
      x = "Date",
      y = "Level (ft)"
    ) +
    theme_cs

Lines

Vertical

ChickWeight %>%
  ggplot() +
    geom_vline(
      xintercept = 15, color = wso::color.wsoCols("dark blue")
    ) +
    geom_point(
      aes(x = Time, y = weight, color = Diet)
    ) +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Vertical (repeated)

ChickWeight %>%
  ggplot() +
    geom_vline(
      xintercept = seq(1, 20, 5), color = wso::color.wsoCols("dark blue"), 
      linetype = "longdash", size = 0.75
    ) +
    geom_point(
      aes(x = Time, y = weight, color = Diet)
    ) +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Horizontal

For annotation, hjust stands for horizontal justification, 0 will be left-justified, 0.5 will be centered, and 1 will be right-justified. For vjust, 0 will be top-justified, 0.5 will be centered, and 1 will be bottom-justified.

ChickWeight %>%
  ggplot() +
    geom_hline(
      yintercept = 100, color = wso::color.wsoCols("red")
    ) +
    geom_point(
      aes(x = Time, y = weight, color = Diet)
    ) +
    annotate(
      "text", x = 0, y = 105, color = "red", fontface = "bold", 
      hjust = 0, vjust = 0,
      label = "Healthy min. at 15 days"
    ) +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Sloped

Manually set

ChickWeight %>%
  ggplot() +
    geom_abline(
      intercept = 40, slope = 4, color = wso::color.wsoCols("dark blue"),
    ) +
    geom_point(
      aes(x = Time, y = weight, color = Diet)
    ) +
    labs(
      title = "Weight of Chicks by Diet Type",
      x = "Days from birth",
      y = "Weight (grams)"
    ) +
    theme_cs

Area

huron %>%
  ggplot() +
    geom_area(
      aes(x = year, y = level ), 
      fill = wso::color.wsoCols("blue")
    ) + 
    labs(
      title = "Level of Lake Huron",
      x = "Date",
      y = "Level (ft)"
    ) +
    theme_cs

We can also adjust the canvas to zoom in on the changes

huron %>%
  ggplot() +
    geom_area(
      aes(x = year, y = level ), 
      fill = wso::color.wsoCols("blue")
    ) + 
    labs(
      title = "Level of Lake Huron",
      x = "Date",
      y = "Level (ft)"
    ) +
    coord_cartesian(
      ylim = c(560, 590)
      # Could also set dynamically
      #ylim = c(min(huron$level) - 10, max(huron$level + 10))
    ) +
    theme_cs

Random adjustments

Lots of information here: https://ggplot2.tidyverse.org/reference/theme.html

Legend inside plot

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status))
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms",
      y = "Pressure (mbar)",
      x = "Wind Speed (mph)"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs +
    theme(
      legend.position = c(.05, .05),
      legend.justification = c("left", "bottom"),
      legend.box.just = "right",
      legend.margin = margin(6, 6, 6, 6),
      legend.background = element_rect(fill = alpha("white", 0.6))
    )

Remove legend

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status)),
      show.legend = FALSE
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms",
      y = "Pressure (mbar)",
      x = "Wind Speed (mph)"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs

Remove Axis

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status))
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs +
    theme(
      legend.position = "bottom",
      axis.title = element_blank(),
      axis.text = element_blank(),
      axis.ticks = element_blank(),
      # axis.title.x = element_blank(),
      # axis.text.x = element_blank(),
      # axis.ticks.x = element_blank(),
      # axis.title.y = element_blank(),
      # axis.text.y = element_blank(),
      # axis.ticks.y = element_blank()
    )

Remove Grid

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status))
    ) +
    scale_y_continuous(
      labels = scales::comma_format()
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms",
      y = "Pressure (mbar)",
      x = "Wind Speed (mph)"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs +
    theme(
      panel.grid.major = element_blank(), 
      panel.grid.minor = element_blank()
    )

Remove Everything

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status)),
      show.legend = FALSE
    ) +
    wso::color.scale_color_wso() +
    theme_cs +
    theme_void()

Adjust Grid

storms %>%
  ggplot() +
    geom_point(
      aes(x = wind, y = pressure, color = factor(status))
    ) +
    labs(
      title = "Pressure vs Wind Speed of Storms",
      y = "Pressure (mbar)",
      x = "Wind Speed (mph)"
    ) +
    wso::color.scale_color_wso(
      name = "Storm Type",
      labels = stringr::str_to_title
    ) +
    theme_cs +
    theme(
      panel.grid.minor = element_line(
        colour = wso::color.wsoCols("dark blue"), 
        size = 0.25
      )
      # panel.grid.major = element_line(
      #   colour = wso::color.wsoCols("dark blue"), 
      #   size = 0.25
      # )
    )